Skip to main content
PMC home page
BMC Medical Research Methodology logoLink to BMC Medical Research Methodology
. 2010 Aug 26;10:77. doi: 10.1186/1471-2288-10-77

Sampling and coverage issues of telephone surveys used for collecting health information in Australia: results from a face-to-face survey from 1999 to 2008

Eleonora Dal Grande 1,✉, Anne W Taylor 1,2
PMCID: PMC2942894  PMID: 20738884

Abstract

Background

To examine the trend of "mobile only" households, and households that have a mobile phone or landline telephone listed in the telephone directory, and to describe these groups by various socio-demographic and health indicators.

Method

Representative face-to-face population health surveys of South Australians, aged 15 years and over, were conducted in 1999, 2004, 2006, 2007 and 2008 (n = 14285, response rates = 51.9% to 70.6%). Self-reported information on mobile phone ownership and usage (1999 to 2008) and listings in White Pages telephone directory (2006 to 2008), and landline telephone connection and listings in the White Pages (1999 to 2008), was provided by participants. Additional information was collected on self-reported health conditions and health-related risk behaviours.

Results

Mobile only households have been steadily increasing from 1.4% in 1999 to 8.7% in 2008. In terms of sampling frame for telephone surveys, 68.7% of South Australian households in 2008 had at least a mobile phone or landline telephone listed in the White Pages (73.8% in 2006; 71.5% in 2007). The proportion of mobile only households was highest among young people, unemployed, people who were separated, divorced or never married, low income households, low SES areas, rural areas, current smokers, current asthma or people in the normal weight range. The proportion with landlines or mobiles telephone numbers listed in the White Pages telephone directory was highest among older people, married or in a defacto relationship or widowed, low SES areas, rural areas, people classified as overweight, or those diagnosed with arthritis or osteoporosis.

Conclusion

The rate of mobile only households has been increasing in Australia and is following worldwide trends, but has not reached the high levels seen internationally (12% to 52%). In general, the impact of mobile telephones on current sampling frames (exclusion or non-listing of mobile only households or not listed in the White Pages directory) may have a low impact on health estimates obtained using telephone surveys. However, researchers need to be aware that mobile only households are distinctly different to households with a landline connection, and the increase in the number of mobile-only households is not uniform across all groups in the community. Listing in the White Pages directory continues to decrease and only a small proportion of mobile only households are listed. Researchers need to be aware of these telephone sampling issues when considering telephone surveys.

Background

With the rapid changes to the telecommunications industry, it is unknown whether telephone surveys can continue to be used to reliably collect representative information regarding health status and health risk behaviours. Telephone surveys, traditionally using landline telephones, have been used to collect and monitor health-related information over the last 30 years [1-3] and have been used to determine the prevalence of chronic conditions, health-related risk behaviours, and assess knowledge, attitudes, and opinions on health issues. Telephone surveys using Computer Assisted Telephone Interviewing (CATI) technology have been seen as a cost-effective and timely method of collecting health information [4-9] and have provided greater standardisation of administration through closer supervision of interviewers compared to traditional face-to-face methods.

In Australia, for the last decade, the coverage of households with a telephone connected (landline) and the adequacy of the sampling frame(s) have been a concern for those involved in epidemiologically-sound telephone surveys. The proportion of people who do not have a landline telephone connected in the household is not uniformly distributed in the population[10,11] and there is difficulty in obtaining a complete sampling frame[12]. With the exception of the Northern Territory, the landline telephone coverage in Australian households has been historically very high (96.8% in South Australia in 2002[10], 94.4% in Australia in 1991[13]). There are two main population sampling methods used in Australia: Australian Electronic White Pages (EWP) directory and Random Digit Dialling (RDD). EWP consists of all landline telephone numbers, names and address details for a household or business. All these telephone numbers are centrally located and routinely listed in the EWP regardless of the telecommunication carrier. Households can opt, at a cost, to not have their telephone number have listed in the EWP (also known as silent numbers). Mobile numbers are not routinely listed in the EWP, so owners can choose, at a cost, to have their mobile number listed in the EWP. This exclusion of unlisted numbers from the sampling frame can have an effect on the estimates for the population in telephone surveys and remains an important concern for researchers [11,12]. RDD methods in Australia are based on the prefixes of the telephone numbers in the EWP to generate a sampling frame in order to include the silent numbers. This method is referred to as list-assisted RDD (LA-RDD). However, these RDD sampling methods do not include mobile numbers in their sampling frame. The differences and similarities between these two methods, in the Australian context, have been described elsewhere [10-12,14].

Since the early 2000 s, international trends have seen households change to new telecommunications technologies, whereby individuals in the household are solely contactable by mobile phones or other means such as Voice over Internet Protocol (VoIP), not by the traditional landline telephone. This has negatively impacted on telephone surveys and sampling methodologies. International studies have shown a dramatic increase in mobile only households (those not having a landline telephone connected to the household). In 2006, 11.8% of households in the United States [15], 52% in Finland [16] and 17% in France [16] were mobile only. In 2002, 13.1% of households in Italy were mobile only households [17].

As a result of these rapid changes both in Australia and worldwide, determining an adequate sampling frame to include these non-traditional telephone numbers and to demarcate geographic locations, is becoming increasingly important. This paper presents how two factors impact household telephone surveys in Australia; the presence of mobile only individuals and the lack of full enumeration of telephone numbers in a telephone directory. Mobile only households are not covered in the RDD sampling frame and unlisted telephone numbers (landlines and mobiles) are not covered in the EWP telephone directory. Both samplings frames exclude people with no mobiles or landline telephones. The aim of this study is to examine the trend of mobile only households and households that have a mobile telephone or landline telephone listing in the EWP, and to describe these groups by various socio-demographics and health indicators to determine the potential bias due to non-coverage in the sample in the Australian context.

Methods

Survey design and sample section

Questions regarding landline telephone, mobile, and internet connections were included in the 1999, 2004, 2006, 2007 and 2008 South Australian Health Omnibus Surveys (HOS)[18,19]. HOS is a multi-stage, systematic, clustered area sample of households conducted face-to-face annually in spring based on the Australian Bureau of Statistics (ABS) collector districts (CDs). The HOS samples included households randomly selected from CDs, from the metropolitan Adelaide area and country towns with a population of 1,000 people or more. Within each CD, a random starting point was selected and from this point 10 households were then selected in a given direction with a fixed skip interval. Hotels, motels, hospitals, hostels and other institutions were excluded from the sample. Approach letters were sent to selected households informing them of the survey. One person aged 15 years or over, who was last to have a birthday, was randomly selected from each household for interview. The interviews were conducted in people's homes by trained interviewers. Up to six call back visits were made to the chosen households to interview the selected person. There was no replacement for non-respondents. Response rates (and sample size) from the surveys were 70.6% (n = 3012) in 1999, 68.4% (n = 2982) in 2004, 54.9% (n = 2969) in 2006, 51.9% (n = 2507) in 2007 and 53.6% (n = 2824) in 2008. Each individual data set was weighted by five year age groups, sex, area (metropolitan Adelaide and SA country) and household size to the most recent ABS Census or Estimated Residential Population for South Australia to provide population estimates.

Data items

Questions about landline telephone connection and listing in the EWP (alphabetic directory of non-silent phone numbers belonging to residential households and businesses which include surname and address details) were included in the 1999, 2004, 2006, 2007 and 2008 surveys. These surveys also included questions on mobile phone ownership and usage in the household. Additional questions on mobile phone listing in the EWP, and future landline and mobile phone ownership plans were included in 2006, 2007 and 2008.

Identical demographic variables included in each survey were age, sex, area of residence, country of birth, education level, marital status, gross annual household income, and work status. The Index of Relative Social Disadvantage (IRSD) developed by the ABS was also calculated to identify the geographical areas that were relatively disadvantaged[20]. The IRSD is a composite measure based on selected Census variables such as income, educational attainment and employment status. The IRSD scores were grouped into quintiles for analysis where the highest quintile comprises postcodes with the highest IRSD scores (most advantaged areas).

Chronic conditions included medically confirmed diabetes, current asthma, arthritis and osteoporosis. Self-reported health risk factor data included smoking status and body mass index (BMI) which was derived from self-reported weight and height and recoded into four categories (underweight, normal weight, overweight and obese) [21]. Mobile only households were defined as households with no landline telephone connected to the house and if mobile phones were currently being used by members of the household.

The questionnaire and methodology for these surveys were approved by the Human Research Ethics Committee of the South Australian Department of Health (SA Health).

Data analyses

Data analysis was conducted using SPSS for Windows Version 15.0. The conventional p value of 0.05 was used as the criterion for statistical significance. To compare prevalence over time (from 1999 to 2008), χ2 test for trend was used for mobile only households, households with landline telephone only connections and households with both a landline telephone connection and mobile. A comparison between 2006, 2007 and 2008 using χ2 tests for trend was undertaken to assess for change in various socio-demographic and health-related variables between mobile only households and households with a landline or a mobile telephone number listed in the EWP.

Separate univariate analyses using χ2 tests were undertaken for 2006, 2007 and 2008, to assess mobile only households, and households with a landline or a mobile telephone number listed in the EWP on a range of socio-demographic and health-related variables.

Results

Table 1 shows the trends of mobile only and landline households from 1999, 2004, 2006 to 2008. The proportion of mobile only households has been steadily increasing over the eight years (χ2trend = 177.01, p < 0.001). There was a statistically significant decline in the proportion of households with landline telephone only connections (χ2trend = 1693.6, p < 0.001) from 44.6% in 1999 to 6.9% in 2008, and a statistically significant increase in households with both a landline telephone connection and mobile (χ2trend = 1188.6, p < 0.001) from 52.7% in 1999 to 83.8% in 2008.

Table 1.

Telephone (landline) and mobile status of household, and household has landline and/or mobile telephone listed in the Australian Electronic White Pages by year

Telephone (landline) and mobile status of household Household has landline and/or at least one mobile telephone listed in directory
No landline telephone or mobile Mobile only household Landline telephone only Landline telephone and mobile Not stated No Yes
Year n % (95% CI) % (95% CI) % (95% CI) % (95% CI) % n % (95% CI) % (95% CI)
1999 3012 1.3 (1.0 - 1.8) 1.4 (1.0 - 1.8) 44.6 (42.9 - 46.4) 52.7 (50.9 - 54.5)
2004 2982 1.2 (0.8 - 1.6) 3.4 (2.8 - 4.1) 95.4 (94.6 - 96.1) * 0.1
2006 2969 0.3 (0.1 - 0.5) 5.2 (4.4 - 6.0) 9.7 (8.7 - 10.8) 84.7 (83.4 - 86.0) 0.1 2961 26.2 (24.6 - 27.8) 73.8 (72.2 - 75.4)
2007 2507 1.1 (0.7 - 1.6) 7.1 (6.1 - 8.1) 11.1 (9.9 - 12.3) 80.6 (79.0 - 82.1) 0.2 2480 28.5 (26.8 - 30.3) 71.5 (69.7 - 73.2)
2008 2824 0.3 (0.2 - 0.6) 8.7 (7.7 - 9.8) 6.9 (6.1 - 7.9) 83.8 (82.4 - 85.1) 0.3 2814 31.3 (29.5 - 32.9) 68.7 (66.7 - 70.1)
Open in a new tab

* In 2004 HOS, households with a landline were not asked if there were any person in the household that had a mobile phone

Respondents were asked if their mobile or landline telephones were listed in the EWP. Of mobile only households, 6.9% had their mobile number listed in the EWP in 2008 (8.0% in 2006; 3.4% in 2007). Of households with both a mobile and landline telephone connected, 7.4% had their mobile number listed in 2008 (7.3% in both 2006 and 2007). Examination of households with a landline telephone connection revealed that 77.0% of these households in 2006, 76.3% in 2007 and 74.1% in 2008, had their landline telephone numbers listed in the EWP (χ2trend = 18.6, p < 0.001). Hence, 68.7% of South Australian households in 2008 had at least a mobile phone and/or landline telephone listed in the EWP (73.8% in 2006, 71.5% in 2007) (Table 1).

When the proportion of mobile only household respondents was compared on selected demographics and health indicators over the three years (2006 to 2008) (Table 2), increased trends were significant for a wide range of variables. When households with a mobile or landline telephone number listed in the EWP (Table 3) were examined over the three years (2006 to 2008), decreasing trends were apparent for a range of variables.

Table 2.

Proportion of people living in mobile only households by selected demographic, health conditions, and health related risk factors from 2006 to 2008

2006 2007 2008 Test for trend
n % (95% CI) P value1 n % (95% CI) P value1 n % (95% CI) P value1 P value2
DEMOGRAPHICS
Gender 0.007 < 0.001 0.149
Male 92/1460 6.3 (5.2 - 7.7) 109/1222 8.9 (7.4 - 10.6) 131/1382 9.4 (8.0 - 11.1) 0.002
Female 62/1509 4.1 (3.2 - 5.2) 68/1285 5.3 (4.2 - 6.7) 114/1433 7.9 (6.6 - 9.4) < 0.001
Age Groups < 0.001 < 0.001 < 0.001
15 to 29 years 73/708 10.4 (8.3 - 12.8) 73/587 12.7 (10.2 - 15.7) 114/571 17.9 (15.2 - 20.9) < 0.001
30 to 44 years 47/777 6.1 (4.7 - 8.1) 67/644 10.5 (8.4 - 13.2) 76/693 11.0 (8.9 - 13.6) 0.001
45 years and over 34/1485 2.3 (1.6 - 3.2) 38/1276 3.0 (2.2 - 4.0) 43/1420 3.0 (2.2 - 4.0) 0.213
Country of Birth 0.935 0.001 < 0.001
Australia 116/2197 5.3 (4.4 - 6.3) 152/1911 8.0 (6.8 - 9.3) 209/2110 9.9 (8.7 - 11.3) < 0.001
UK & Ireland 17/354 4.9 (3.1 - 7.7) 5/242 1.9 # 6/296 2.1 (1.0 - 4.5) 0.040
Other 21/418 5.0 (3.3 - 7.5) 20/354 5.8 (3.8 - 8.7) 29/409 7.0 (4.9 - 9.8) 0.218
Marital status a < 0.001 < 0.001 < 0.001
Married/defacto 66/1862 3.5 (2.8 - 4.5) 53/1534 3.5 (2.7 - 4.5) 111/1765 6.3 (5.2 - 7.5) < 0.001
Separated/divorced 24/248 9.6 (6.5 - 13.9) 36/227 16.0 (11.8 - 21.3) 27/237 11.3 (7.8 - 15.9) 0.560
Widowed 3/156 1.6 # 4/141 3.1 # 5/149 3.6 (1.6 - 7.9) 0.286
Never married 62/694 8.9 (7.0 - 11.2) 83/602 13.8 (11.3 - 16.8) 101/661 15.2 (12.6 - 18.1) < 0.001
Educational Attainment b 0.007 < 0.001 0.002
None to secondary schooling 81/1385 5.8 (4.7 - 7.2) 107/1234 8.7 (7.2 - 10.4) 134/1281 10.4 (8.9 - 12.2) < 0.001
Trade qualifications,
Certificate, Diploma		 64/1086 5.8 (4.6 - 7.4) 60/843 7.1 (5.6 - 9.1) 82/1015 8.1 (6.6 - 9.9) 0.042
Bachelor Degree or higher 10/488 2.1 (1.1 - 3.8) 10/428 2.4 (1.3 - 4.3) 27/514 5.2 (3.6 - 7.5) 0.005
Area of residence 0.089 < 0.001 < 0.001
Metropolitan 101/2123 4.8 (3.9 - 5.8) 107/1847 5.8 (4.8 - 7.0) 160/2152 7.4 (6.4 - 8.6) < 0.001
Country 53/846 6.3 (4.8 - 8.1) 70/660 10.6 (8.5 - 13.2) 84/663 12.6 (10.3 - 15.4) < 0.001
Annual household income 0.001 0.001 0.001
Up to $20,000 25/387 6.5 (4.4 - 9.4) 38/335 11.2 (8.3 - 15.1) 34/342 9.8 (7.1 - 13.4) 0.105
$20,001-$40,000 42/509 8.2 (6.1 - 10.9) 33/437 7.6 (5.5 - 10.5) 44/431 10.2 (7.7 - 13.4) 0.297
$40,001-$60,000 24/451 5.3 (3.6 - 7.8) 28/383 7.2 (5.0 - 10.2) 41/353 11.7 (8.8 - 15.5) < 0.001
$60,001-$80,000 11/350 3.1 (1.7 - 5.4) 22/274 8.0 (5.3 - 11.8) 23/326 7.0 (4.7 - 10.3) 0.027
$80,001 or more 20/675 2.9 (1.9 - 4.5) 22/600 3.7 (2.5 - 5.5) 42/784 5.4 (4.0 - 7.2) 0.018
Not stated 33/597 5.6 (4.0 - 7.7) 34/477 7.2 (5.2 - 9.9) 61/580 10.4 (8.2 - 13.1) 0.002
Work status c < 0.001 < 0.001 < 0.001
Work full time 69/1109 6.2 (4.9 - 7.8) 76/920 8.2 (6.6 - 10.2) 109/1091 10.0 (8.4 - 11.9) 0.001
Work part time 30/566 5.2 (3.7 - 7.4) 27/455 5.9 (4.1 - 8.5) 40/494 8.2 (6.1 - 10.9) 0.055
Home Duties 16/272 5.7 (3.5 - 9.2) 28/255 10.9 (7.7 - 15.4) 26/227 11.6 (8.1 - 16.5) 0.019
Unemployed 16/79 20.9 (13.4 - 31.1) 22/76 28.6 (19.7 - 39.6) 19/77 24.1 (15.9 - 34.8) 0.634
Retired 3/578 0.6 # 6/505 1.2 (0.6 - 2.6) 10/575 1.8 (1.0 - 3.2) 0.068
Student 9/220 4.1 (2.2 - 7.6) 9/217 4.2 (2.2 - 7.7) 23/227 10.0 (6.7 - 14.6) 0.009
Other/Not working because
of work related injury		 11/104 10.8 (6.2 - 18.2) 10/79 12.3 (6.8 - 21.3) 15/123 12.3 (7.6 - 19.3) 0.735
SEIFA IRSD Quintiles d 0.001 < 0.001 < 0.001
Lowest/low (most
disadvantaged)		 90/1293 7.0 (5.7 - 8.5) 118/1124 10.5 (8.9 - 12.4) 159/1247 12.7 (11.0 - 14.7) < 0.001
Middle 25/578 4.3 (2.9 - 6.3) 31/496 6.3 (4.5 - 8.8) 31/520 5.9 (4.2 - 8.3) 0.314
High/Highest (least
disadvantaged)		 39/1088 3.6 (2.6 - 4.9) 25/882 2.8 (1.9 - 4.1) 55/1048 5.3 (4.1 - 6.8) 0.017
HEALTH CONDITIONS
AND HEALTH RELATED
RISK FACTORS
Diabetes 13/197 6.4 (3.8 - 10.8) 0.413 6/168 3.3 (1.5 - 7.3) 0.051 24/214 11.0 (7.5 - 15.9) 0.181 0.053
Arthritis 18/694 2.6 (1.6 - 4.0) < 0.001 27/595 4.6 (3.2 - 6.5) 0.006 31/707 4.3 (3.1 - 6.1) < 0.001 0.092
Osteoporosis 2/184 1.3 # 0.013 6/168 3.3 (1.5 - 7.3) 0.051 3/158 2.1 # 0.002 0.618
Asthma (current) 37/364 10.1 (7.4 - 13.6) < 0.001 31/290 10.7 (7.7 - 14.8) 0.009 47/385 12.1 (9.2 - 15.8) 0.009 0.377
Smoking status e < 0.001 < 0.001 < 0.001
Non/Ex smoker 78/2357 3.3 (2.7 - 4.1) 87/2009 4.3 (3.5 - 5.3) 138/2264 6.1 (5.2 - 7.2) < 0.001
Current smoker 76/611 12.4 (10.1 - 15.3) 90/495 18.2 (15.1 - 21.9) 106/551 19.2 (16.2 - 22.7) 0.002
Body mass index (BMI) f 0.058 < 0.001 0.049
Underweight <18.5 3/64 4.8 # 14/76 18.9 (11.7 - 29.1) 4/63 5.7 # 0.998
Normal 18.5-24.9 65/1144 5.7 (4.5 - 7.2) 79/925 8.5 (6.9 - 10.5) 99/1005 9.8 (8.2 - 11.8) < 0.001
Overweight 25.0-29.9 28/848 3.3 (2.3 - 4.7) 47/769 6.1 (4.6 - 8.0) 52/820 6.4 (4.9 - 8.2) 0.005
Obese 30.0+ 33/565 5.9 (4.2 - 8.2) 20/471 4.2 (2.7 - 6.4) 46/560 8.3 (6.3 - 10.8) 0.081
Overall 154/2969 5.2 (4.5 - 6.1) 177/2507 7.1 (6.1 - 8.1) 244/2816 8.7 (7.7 - 9.8) < 0.001
Open in a new tab

1 p values that are bold denotes statistical significance at the 0.05 level from the X2 test for that variable;

2 p values that are bold denotes statistical significance at the 0.05 level from the X2 test for trend for the 2006 to 2008 time period for that category; CI confidence interval of proportion

a 10 cases missing for 2006, 4 cases missing for 2007 and 7 cases missing for 2008; b 9 cases missing for 2006, 1 case missing for 2007 and 9 cases missing for 2008; c 42 cases missing for 2006, 1 case missing for 2007 and 10 cases missing for 2008; d 10 cases missing for 2006, 5 cases missing for 2007; e 2 cases missing for 2006, 2 cases missing for 2007; f 348 cases missing for 2006, 265 cases missing for 2007 and 369 cases for 2008

Table 3.

Proportion of people living in households where mobile phone or landline telephone is listed in the White Pages by selected demographic, health conditions, and health related risk factors from 2006 to 2008

2006 2007 2008 Test for
trend
n % (95% CI) P value1 n % (95% CI) P value1 n % (95% CI) P value1 P value2
DEMOGRAPHICS
Gender 0.625 0.557 0.842
Male 1106/1507 74.2 (71.9 - 76.4) 915/1271 70.9 (68.3 - 73.4) 989/1436 68.5 (66.0 - 70.9) 0.006
Female 1079/1454 73.4 (71.1 - 75.6) 857/1209 72.0 (69.5 - 74.4) 944/1379 68.9 (66.4 - 71.2) 0.001
Age Groups < 0.001 < 0.001 < 0.001
15 to 29 years 440/707 62.2 (58.6 - 65.7) 317/571 55.6 (51.5 - 59.6) 306/570 53.3 (49.6 - 57.0) 0.002
30 to 44 years 554/773 71.7 (68.4 - 74.7) 429/636 67.4 (63.6 - 70.9) 438/692 63.3 (59.6 - 66.8) 0.001
45 to 59 years 563/751 75.0 (71.8 - 78.0) 466/627 74.4 (70.9 - 77.7) 493/676 73.0 (69.5 - 76.2) 0.384
60 years and over 628/730 86.1 (83.4 - 88.4) 560/647 86.7 (83.8 - 89.1) 628/746 84.3 (81.5 - 86.7) 0.313
Country of Birth 0.639 0.004 < 0.001
Australia 1627/2191 74.3 (72.4 - 76.1) 1367/1886 72.5 (70.4 - 74.4) 1460/2104 69.4 (67.4 - 71.3) < 0.001
UK & Ireland 256/353 72.7 (67.8 - 77.0) 179/241 74.4 (68.5 - 79.5) 221/298 74.3 (69.0 - 78.9) 0.633
Other 302/417 72.4 (68.0 - 76.5) 227/353 64.2 (59.0 - 69.0) 251/413 60.9 (56.2 - 65.5) < 0.001
Marital status a < 0.001 < 0.001 < 0.001
Married/defacto 1465/1860 78.8 (76.8 - 80.5) 1188/1525 77.9 (75.8 - 79.9) 1301/1764 73.8 (71.7 - 75.8) < 0.001
Separated/divorced 155/246 62.9 (56.7 - 68.7) 138/222 61.9 (55.3 - 68.0) 142/236 60.0 (53.7 - 66.1) 0.518
Widowed 118/154 76.5 (69.2 - 82.5) 110/141 78.2 (70.7 - 84.2) 118/148 79.9 (72.7 - 85.5) 0.483
Never married 444/690 64.4 (60.8 - 67.9) 336/588 57.1 (53.1 - 61.0) 370/659 56.1 (52.3 - 59.9) 0.002
Educational Attainment b 0.032 0.494 < 0.001
None to secondary
schooling		 990/1378 71.8 (69.4 - 74.1) 854/1216 70.2 (67.6 - 72.7) 840/1275 65.9 (63.2 - 68.4) 0.001
Trade qualifications,
Certificate, Diploma		 826/1085 76.1 (73.4 - 78.5) 611/836 73.1 (70.0 - 76.0) 741/1016 73.0 (70.2 - 75.6) 0.105
Bachelor Degree or
higher		 361/488 74.0 (70.0 - 77.7) 307/427 72.0 (67.5 - 76.0) 348/514 67.7 (63.6 - 71.6) 0.027
Area of residence < 0.001 < 0.001 0.001
Metropolitan 1523/2120 71.8 (69.9 - 73.7) 1264/1830 69.1 (66.9 - 71.2) 1443/2152 67.1 (65.0 - 69.0) 0.001
Country 663/841 78.8 (75.9 - 81.5) 508/650 78.2 (74.9 - 81.2) 490/663 74.0 (70.5 - 77.2) 0.030
Annual household
income 		0.121 0.005 0.039
Up to $20,000 486/675 74.5 (69.9 - 78.6) 446/597 66.2 (60.9 - 71.1) 569/784 64.1 (58.9 - 69.1) 0.812
$20,001-$40,000 273/350 74.5 (70.6 - 78.1) 206/273 74.3 (70.0 - 78.2) 229/328 68.7 (64.1 - 72.9) 0.013
$40,001-$60,000 341/449 75.9 (71.8 - 79.6) 269/380 70.8 (66.0 - 75.1) 241/353 68.3 (63.3 - 72.9) 0.016
$60,001-$80,000 379/509 78.1 (73.5 - 82.2) 322/434 75.6 (70.1 - 80.3) 295/429 69.8 (64.7 - 74.5) 0.049
$80,001 or more 284/381 72.0 (68.5 - 75.2) 218/330 74.6 (71.0 - 78.0) 216/336 72.6 (69.4 - 75.6) 0.003
Not stated 422/597 70.8 (67.0 - 74.3) 312/467 66.7 (62.3 - 70.8) 384/585 65.6 (61.7 - 69.3) 0.058
Work status c < 0.001 < 0.001 < 0.001
Work full time 796/1109 71.8 (69.1 - 74.4) 641/909 70.4 (67.4 - 73.3) 751/1091 68.9 (66.0 - 71.5) 0.127
Work part time 422/565 74.7 (71.0 - 78.2) 327/450 72.7 (68.4 - 76.6) 346/494 70.0 (65.8 - 73.9) 0.084
Home Duties 198/270 73.2 (67.7 - 78.2) 170/252 67.2 (61.2 - 72.7) 134/222 60.2 (53.7 - 66.4) 0.002
Unemployed 39/78 50.3 (39.4 - 61.1) 35/74 47.8 (36.8 - 59.0) 36/76 47.2 (36.4 - 58.3) 0.707
Retired 489/574 85.2 (82.1 - 87.9) 435/504 86.2 (82.9 - 88.9) 482/575 83.8 (80.6 - 86.6) 0.502
Student 141/220 64.3 (57.8 - 70.3) 129/215 59.8 (53.2 - 66.2) 114/226 50.5 (44.1 - 57.0) 0.003
Other/Not working
because of work
related injury		 71/104 68.1 (58.6 - 76.3) 35/74 48.1 (37.1 - 59.3) 67/121 55.4 (46.5 - 64.0) 0.067
SEIFA IRSD
Quintiles d 		0.080 < 0.001 < 0.001
Lowest/low (most
disadvantaged)		 827/1088 72.0 (69.5 - 74.4) 666/876 67.5 (64.7 - 70.2) 804/1237 64.9 (62.2 - 67.5) 0.001
Middle 421/575 73.3 (69.5 - 76.7) 359/495 72.5 (68.4 - 76.2) 368/520 70.7 (66.7 - 74.5) 0.634
High/Highest (least
disadvantaged)		 927/1288 76.0 (73.4 - 78.5) 745/1103 76.0 (73.1 - 78.7) 761/1048 72.6 (69.9 - 75.2) 0.123
HEALTH CONDITIONS
AND HEALTH RELATED
RISK FACTORS
Diabetes 144/197 73.3 (66.7 - 79.0) 0.863 132/168 78.7 (71.9 - 84.2) 0.033 158/214 73.9 (67.7 - 79.4) 0.104 0.994
Arthritis 550/693 79.4 (76.2 - 82.2) < 0.001 452/593 76.2 (72.6 - 79.5) 0.003 534/705 75.8 (72.5 - 78.8) < 0.001 0.090
Osteoporosis 161/184 87.4 (81.9 - 91.5) < 0.001 132/168 78.7 (71.9 - 84.2) 0.033 111/158 70.3 (62.8 - 76.9) 0.703 < 0.001
Asthma (current) 256/364 70.3 (65.4 - 74.8) 0.105 193/283 68.3 (62.6 - 73.4) 0.203 245/385 63.6 (58.7 - 68.3) 0.017 0.048
Smoking status e < 0.001 < 0.001 < 0.001
Non/Ex smoker 1817/2351 77.3 (75.5 - 78.9) 1492/1994 74.9 (72.9 - 76.7) 1642/2262 72.6 (70.7 - 74.4) < 0.001
Current smoker 368/608 60.5 (56.6 - 64.3) 280/486 57.6 (53.2 - 61.9) 291/544 53.6 (49.4 - 57.7) 0.014
Body mass index
(BMI) f 		0.011 < 0.001 < 0.001
Underweight <18.5 44/64 68.9 (56.8 - 78.9) 36/76 47.7 (36.9 - 58.7) 43/63 67.2 (54.9 - 77.5) 0.963
Normal 18.5-24.9 816/1140 71.5 (68.8 - 74.1) 635/923 68.7 (65.7 - 71.6) 641/1005 63.8 (60.8 - 66.7) < 0.001
Overweight 25.0-29.9 657/847 77.6 (74.7 - 80.3) 569/762 74.7 (71.5 - 77.6) 602/820 73.5 (70.4 - 76.4) 0.053
Obese 30.0+ 428/565 75.8 (72.1 - 79.2) 350/463 75.6 (71.5 - 79.3) 402/560 71.7 (67.9 - 75.3) 0.108
Overall 2186/2961 73.8 (72.2 - 75.4) 1773/2480 71.5 (69.7 - 73.2) 1993/2814 68.7 (66.9 - 70.4) < 0.001
Open in a new tab

1 p values that are bold denotes statistical significance at the 0.05 level from the X2 test for that variable; 2 p values that are bold denotes statistical significance at the 0.05 level from the X2 test for trend for the 2006 to 2008 time period for that category; CI confidence interval of proportion

a 10 cases missing for 2006, 4 cases missing for 2007 and 7 cases missing for 2008; b 9 cases missing for 2006, 1 case missing for 2007 and 9 cases missing for 2008; c 42 cases missing for 2006, 1 case missing for 2007 and 10 cases missing for 2008; d 10 cases missing for 2006, 5 cases missing for 2007; e 2 cases missing for 2006; f 345 cases missing for 2006, 265 cases missing for 2007 and 366 cases for 2008

When examined by selected demographics for 2006, 2007 and 2008 (Table 2), respondents who lived in a mobile only household were statistically significantly more likely to be in the younger age groups, separated, divorced or never married, unemployed, born in Australia, have at least obtained secondary schooling, living in rural areas of South Australia, from low income households or from low SES areas of South Australia, and statistically significantly less likely to be in the older age groups, widowed, married or in a defacto relationship, born in UK/Ireland, living in metropolitan Adelaide, from high income households ($80,000 or more per annum), retired, to have a bachelor degree or higher and from high SES areas of South Australia. In terms of health conditions and health related risk factors, respondents who live in mobile only households were statistically significantly more likely to be current smokers, classified as having normal BMI or diagnosed with current asthma, and were statistically significantly less likely to be classified as overweight.

Respondents from households with a mobile or landline telephone number listed in the EWP (Table 3) were statistically significantly more likely to be in the older age groups, married or in a defacto relationship or widowed, retired, living in rural areas and from high SES areas of South Australia, and statistically significantly less likely to be in the younger age groups, never married, separated or divorced, a student, unemployed or not working because of an injury, living in metropolitan Adelaide or from low SES areas of South Australia. They were also statistically significantly more likely to be classified as being overweight, to have arthritis, or statistically significantly less likely to be current smokers and have normal BMI.

Further questions were asked to determine the likelihood of people with a landline telephone switching to being a mobile only household. Overall, 6.9% in 2006, 5.9% in 2007, and 8.1% in 2008 indicated that they were 'very likely' while 11.0%, 10.8%, and 12.1% respectively indicated they were 'somewhat likely' to discontinue their landline connection.

Discussion

This study has shown, using large representative surveys, the proportion of mobile only households has been increasing in Australia and is following international trends. The prevalence of mobile only households in South Australia among people aged 15 years and over (8.7% in 2008), is not as high as other international studies: 11.8% in the United States[15]; 52% in Finland[16] and 17% in France in 2006[16] and, 13.1% in Italy in 2002[17]. However, the pattern of increasing prevalence remains the same and there are also changes among a range of demographic, health status and health risk behaviours groups. The prevalence of households with neither a mobile phone nor landline telephone has remained low and is likely to have a minimal effect on surveys using mobile phone or landline telephones. However, the mobile only prevalence may increase in South Australia over the next few years since 8% of survey respondents indicated they were very likely to become a mobile only household.

From this study, using LA-RDD methodology to generate a sampling frame to include unlisted landline telephone numbers excludes mobile only households as well as households with no landline telephone connection which is 9% of the population. This could be considered small [22] and one could argue that excluding this group would have minimal impact on health estimates. However, the results presented in this study indicate that mobile only households have different demographic characteristics to households with landline and/or mobiles. These demographic differences are similar to US studies [15,23] with a higher proportion of males, younger people, people who are unemployed, separated, divorced or never married, people living in rural areas of South Australia, and low SES households (low income households and reside in the most disadvantaged areas) living in mobile only households. From this study, in terms of health indicators, people classified as overweight, having current asthma and current smokers would also be under-represented in these surveys.

There are some data quality and collection issues that need to be taken into account when including mobile telephones into the sample frame. One is the location or the situation of the respondent at the time of the interview: respondents may choose not to answer a call to save battery life; answering a call which may incur a cost to both the respondent and the researcher (if the respondent is overseas the fee may be much higher depending on distance from Australia and contractual agreement with individual telecommunication providers); and safety and legal issues, eg the respondent may be driving and using their mobile (texting and talking) at the same time which is illegal in Australia [16]. A study conducted in the US [24] found that those respondents who participated in the survey using a mobile phone, 56% were at home while undertaking the survey, 14% were driving and 13% were at work. The remaining respondents were at other locations such as in public areas, in another person's home, in a car but not driving or on holidays. Another issue found in this US study [24] was the higher proportion of calls to mobile phones resulting in ineligible respondents due to age (people excluded if less than 18 years of age), a lower response rate than calls to landline telephones and a higher refusal rate.

Furthermore, the selection of the respondent differs between mobile and landline telephones. The mobile telephone is usually individually owned and accessed by that one individual most of the time, compared to landline telephones that belong to a household which may be accessed by one or more people. Hence, consideration needs to be given when sampling strategies in terms of randomly selecting a single person to interview versus a number of eligible people in a household [16].

This study has highlighted the need to acquire a representative sampling frame and sampling methodology for household telephone (landline) surveys that minimises selection bias and is efficient in terms of administration and cost. With landline telephone numbers, the majority of the telephone numbers are listed in the EWP and the prefix of the telephone numbers are geographically based. Mobile telephones are the opposite; they are rarely listed (7.3% of mobile telephone users found in this study) and the number structure does not provide any details of geographical location, hence making it difficult to generate a sampling frame similar to current cost effective RDD methods. The large proportional difference in the EWP directory listing between landline and mobile telephone numbers would be mainly due to the options provided to the owners: owners of landline telephone need to pay to have their telephone numbers not listed in the EWP, and owners of mobile telephone need to pay to have their mobile telephone numbers in the EWP. Hence EWP samples are likely to continue to have a small proportion (6.9% in 2008) of mobile only households in the sampling frame. According to these results, if the option is to sample from the EWP, approximately 30% of the population will be excluded, particularly young people, those who have never married, those who reside in rural areas, people on lower income levels, the unemployed and students. In terms of health indicators, people in the normal weight range and current smokers could be under-represented.

Another emerging technology that has not been examined in this study is VoIP (Voice over Internet Protocol). In Australia, the impact of VoIP on sampling frames is not known. VoIP is seen as a cost effective system that utilises broadband data lines. Similar to mobile phones, the structure of VoIP telephone numbers (or also known as virtual number) are not geographically based and owners have the option of listing their VoIP telephone number in the EWP directory. More research is required on the uptake of VoIP including usage and impact on sampling frames.

The results of this study are potentially biased due to survey non-response. The response rates from these surveys (51.9% to 70.6%) could be considered moderately acceptable for a population survey of this kind. With increasingly inaccessible buildings (eg locked gates), busy lifestyles, and security and privacy concerns, an ongoing impact on response rates is expected, following patterns and trends interstate and overseas [25]. The unweighted age distribution had a higher proportion of older people and a lower proportion of younger people. This indicates the proportion of mobile only households could be under-estimated, and listings in the EWP over-estimated. Another limitation is the self-reported nature of this study. People might not want to divulge that they have a landline or mobile phone that is listed in the EWP because they want to avoid telephone calls from telemarketers or researchers [22] resulting in an under-estimation of telephone listings.

What does this mean for telephone (landline) surveys? Researchers need to be aware of the rapid changes in the telecommunication industry that potentially have an impact on collecting representative and reliable data on health-related issues using household telephone (landline) surveys. Studies like this are important because of the increasing need to monitor public health issues in a timely manner in an environment with limited and sometimes conflicting resources. Within these limits, there is a need to determine valid and reliable methods to verify the health estimates used for policy, planning of resources, and evaluation of health promotion interventions. Further research is needed in the area of mobile telephones such as how often the mobile is turned on, whether telephone calls are made more on the mobile or landline, and the likelihood of completing a health survey on a mobile telephone. Further Australian research is also required in terms of different weighting or post-survey adjustment strategies (eg raking) [26], improved sampling strategies [27] and the advantages and disadvantages of mixed mode surveying [28] (such as telephone, face-to-face, mail or internet), in order to improve the coverage of the sampling frame and minimise bias.

Conclusion

Coverage of households with a telephone connected (landline) and the adequacy of the sampling frame(s) have been a concern for those involved in epidemiologically-sound telephone surveys. The rate of mobile only households in South Australia has been increasing and is following worldwide trends but has not reached the high levels seen internationally (12% to 52%). Presently, the impact of mobile telephones on current sampling frames (exclusion of mobile only households or non-listings in the White Pages directory) may be small in relation to the health estimates obtained using telephone surveys. However, researchers need to be aware that mobile only households have distinctly different characteristics compared to households with a landline connection and the increase in the number of mobile-only households is not uniform across all groups in the community. Listing in the White Pages directory is continuing to decrease and only a small proportion of mobile only households are listed. Researchers need to be aware of these telephone sampling issues when considering telephone surveys.

Abbreviations

ABS: Australian Bureau of Statistics; BMI: Body Mass Index; CD: Collector Districts; EWP: Electronic White Pages; IRSD: Index of Relative Social Disadvantage; LA-RDD: List-assisted Random Digit Dialling; RDD: Random Digit Dialling; SPSS: Statistical Package for Social Sciences; VOIP: Voice over Internet Protocol

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

EDG was responsible for the intellectual planning of the project, conception and design of the study, oversight of the Health Omnibus Survey, analysis of data, and interpretation of the data, and drafting of the article. AT was responsible for the intellectual planning of the project, oversight of the Health Omnibus Survey, interpretation of the data and revising of the paper for critical important intellectual content. All authors read and approved the final manuscript.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/10/77/prepub

Contributor Information

Eleonora Dal Grande, Email: eleonora.dalgrande@health.sa.gov.au.

Anne W Taylor, Email: anne.taylor@health.sa.gov.au.

Acknowledgements

With thanks to Melissa Atkinson, Population Research & Outcome Studies Unit, SA Health, for assistance with data analysis, Gillian Leach, CEO, Arthritis SA, Dr Patrick Phillips, Endocrinology Unit, The Queen Elizabeth Hospital, Professor Richard Ruffin, University of Adelaide for providing health data from their questions in the HOS, and Tobacco Control Research and Evaluation Program for their smoking data.

References

  1. Lepkowski JM, Tucker C, Brick JM, de Leeuw ED, Japec L, Lavrakas PJ, Link MW, Sangster RL. Advances in Telephone Survey Methodology. New Jersey: John Wiley & Sons; 2008. [Google Scholar]
  2. McQueen D. A world behaving badly: the global challenge for behavioral surveillance. Am J Public Health. 1999;89(9):1312–1314. doi: 10.2105/AJPH.89.9.1312. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. McQueen D, Ruska P. Global behavioral risk factor surveillance. Plenum: Kuwer Academics; 2003. [Google Scholar]
  4. New South Wales Population Health Survey. Centre for Epidemiology and Research, NSW Department of Health, NSW, Australia; http://www.health.nsw.gov.au/public-health/survey/hsurvey.html [Google Scholar]
  5. Queensland Health. Queensland Omnibus Survey. Questionnaire and Population Weighted Primary Results. Epidemiology Services Unit, Health Information Branch, Queensland, Australia; 2005. [Google Scholar]
  6. Brief reports. SA Department of Health, South Australia, Australia; 2004. The South Australian Monitoring and Surveillance System Survey Methodology 2004.http://www.health.sa.gov.au/PROS/ [Google Scholar]
  7. Taylor A. Chronic disease surveillance in South Australia. Public Health Bulletin. 2006;3:6–9. http://www.health.sa.gov.au/pehs/publications/PHB-chron-disease-ed3-06.pdf [Google Scholar]
  8. Victoria Department of Human Services. Victorian Population Health Survey, Rural and Regional Health and Aged Services. Department of Human Services, Victoria, Australia; 2006. http://www.health.vic.gov.au/healthstatus/vphs.htm [Google Scholar]
  9. Western Australian Health and Wellbeing Surveillance System, Health Outcomes Assessment Unit, Epidemiology Branch, Information Management and Reporting Directorate, Health Information Division, Department of Health, Western Australia, Australia. 2006. http://www.health.wa.gov.au/publications/pop_surveys.cfm
  10. Taylor AW, Wilson DH, Wakefield M. Differences in health estimates using telephone and door-to-door survey methods - a hypothetical exercise. Aust N Z J Public Health. 1998;22(2):223–226. doi: 10.1111/j.1467-842X.1998.tb01177.x. [DOI] [PubMed] [Google Scholar]
  11. Dal Grande E, Taylor A, Wilson D. Is there a difference in health estimates between people with listed and unlisted telephone numbers? Aust N Z J Public Health. 2005;29(5):448–456. doi: 10.1111/j.1467-842X.2005.tb00225.x. [DOI] [PubMed] [Google Scholar]
  12. Wilson D, Starr G, Taylor A, Dal Grande E. Random digit dialling and Electronic White pages samples compared: demographic profiles and health estimates. Aust N Z J Public Health. 1999;23:627–633. doi: 10.1111/j.1467-842X.1999.tb01549.x. [DOI] [PubMed] [Google Scholar]
  13. Australian Bureau of Statistics. Household telephone connections: Australia August 1991. Catalogue No. 4110.0. Commonwealth of Australia, Canberra; 1991. [Google Scholar]
  14. Yang B, Eyeson-Annan M. Does sampling using random digit dialling really cost more than sampling from telephone directories: Debunking the myths. BMC Med Res Methodol. 2006;6:6. doi: 10.1186/1471-2288-6-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Blumberg S, Luke JV. Wireless substitution: early release of estimates based on data from the National Health Interview Survey, July - December 2006. National Center for Health Statistics; 2007. http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless200705.pdf [Google Scholar]
  16. Kuusela V, Callegaro M, Vehovar V. In: Advances in Telephone Survey Methodology. Lepkowski JM, Tucker C, Brick JM, de Leeuw ED, Japec L, Lavrakas PJ, Link MW, Sangster RL, editor. New Jersy: John Wiley & Sons; 2008. The influence of mobile telephone on telephone surveys; pp. 87–112. [Google Scholar]
  17. Callegaro M, Poggio T. Where can I call you? The "Mobile (Phone) Revolution" and its impact on survey research and coverage error: a discussion of the Italian case. Proceedings ISA RC33 Sixth International Conference on Logic and Methodology: "Recent Developments and Applications in Social Research Methodology", Amsterdam. 2004. http://eprints.biblio.unitn.it/archive/00000680/01/Callegaro_Poggio_RC33.pdf
  18. Wilson D, Wakefield M, Taylor A. The South Australian Health Omnibus Survey. Health Promot J of Aust. 1992;2:47–49. [Google Scholar]
  19. Taylor A, Dal Grande E, Wilson D. The South Australian Health Omnibus Survey 15 years on: has public health benefited? Public Health Bulletin. 2006;3:30–32. http://www.health.sa.gov.au/pehs/publications/PHB-chron-disease-ed3-06.pdf [Google Scholar]
  20. Australian Bureau of Statistics. Socio-economic indexes for areas, Australia, 2001. Commonwealth of Australia, Canberra; 2003. [Google Scholar]
  21. WHO (World Health Organization) Obesity: Preventing and managing the global epidemic. Geneva: WHO; 2000. [PubMed] [Google Scholar]
  22. Blumberg SJ, Luke JV, Cynamon ML. Telephone coverage and health survey estimates: evaluating the need for concern about wireless substitution. Am J Public Health. 1999;96(5):926–931. doi: 10.2105/AJPH.2004.057885. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Tucker C, Brick JM, Meekins B. Household telephone service and usage patterns in the United States in 2004: implications for telephone samples. Public Opin Q. 2007;71(1):3–22. doi: 10.1093/poq/nfl047. [DOI] [Google Scholar]
  24. Brick JM, Brick P, Dipko S, Presser S, Tucker C, Yuan YY. Cell phone survey feasibility in the US: sampling and calling cell numbers versus landline numbers. Public Opin Q. 2007;71(5):23–39. doi: 10.1093/poq/nfl040. [DOI] [Google Scholar]
  25. Groves RM, Fowler FJ, Couper MP, Lepkowski JM, Singer E, Tourangeau R. Survey Methodology. New Jersey: John Wiley & Sons; 2004. pp. 184–187. [Google Scholar]
  26. Battaglia MP, Frankel MR, Link M. An examination of poststratification techniques for the Behavioral Risk Factor Surveillance System. Proceedings of the Survey Research Methods Section American Statistical Association. 2006. pp. 2727–2733.
  27. Improvements to the Behavioral Risk Factor Surveillance System (BRFSS) Methodology, Design, and Implementation. Centers for Chronic Disease Control and Prevention National, Center for Chronic Disease Prevention and Health Promotion, USA; http://www.cdc.gov/BRFSS/pdf/BRFSS_Methodology_Brochure.pdf [Google Scholar]
  28. Dollman DA, Smyth JD, Christian LM. Internet, Mail, and Mixed-mode Surveys: The Tailored Design Method. 3. New Jersey: J Wiley & Sons; 2008. [Google Scholar]

Articles from BMC Medical Research Methodology are provided here courtesy of BMC

RESOURCES